1. Field of the Invention
The present invention relates to optimally configuring and then ceasing training in neural networks of a class that can be described as monotonic, asymptotic, and single-objective functions (MASOF) with or without an inflection point. This type of function is best described by example, and an example using the prediction of hydrocarbon producing areas and hydrocarbon non-producing areas directly from seismic data (U.S. Pat. No. 6,236,942) is used to illustrate the system and method of the present invention.
However, the method disclosed is also applicable to a wide range of applications other than those specifically taught herein. Many other applications will be apparent to those skilled in the pertinent art. Accordingly, it is intended by the appended claims to cover all such applications as fall within the true spirit and scope of the present invention.
2. Description of the Prior Art
The present invention relates to a system and method for ceasing the training of certain neural networks at the optimal training point that is most consistent with the objective of the neural network developer. In particular, one inventive concept of the present invention is the removal of the necessity in traditional practice to divide at least a portion of the data into training, test, and validation data sets. This traditional and commonly known practice of dividing some of the data into training, test, and validation data sets is described in a large number of patents including U.S. Pat. No. 6,236,942, “System and Method for Delineating Spatially Dependent Objects, such as Hydrocarbon Accumulations from Seismic Data”, that is included herein by reference. There has been a long felt need to be able to dispense with this burdensome and time-consuming practice and, this is accomplished in the case of the present invention.
One of the characteristics of neural networks is the frequent requirement to have to train the networks for long periods of time. Prior to the issuance of U.S. Pat. No. 6,119,112, “Optimum Cessation of Training in Neural Networks”, neural networks were commonly trained to the point where the average sum-squared error on the training set was reduced to a given level, or a predetermined number of iterations was exceeded. Thus, there was a long existing need in the art to dynamically determine the point at which further training no longer made any improvement in the predictive or classification ability of the neural network. The techniques taught in U.S. Pat. No. 6,119,112 accomplished this and these techniques are extended and enhanced by the present invention for a particular class of neural networks that can be configured to ease the task of determining the optimal cessation of training point as well as improve the accuracy of neural networks across a wide range of applications. How to carry out the configuration of neural networks to take advantage of other inventive techniques disclosed in the present invention is one inventive concept addressed by the present invention. U.S. Pat. No. 6,119,112 is an example of the state of the art prior to the present invention.